| name | manus-swarm |
| description | A multi-agent orchestration system that replicates and improves upon Kimi 2.5's agent swarm capabilities. Use for complex tasks requiring parallel processing, task decomposition, and result synthesis. Embodies the Boris AI Productivity Architect methodology for managing an AI workforce. |
Manus Swarm
Overview
This skill provides the framework for using the Manus Swarm, a multi-agent orchestration system designed for complex task execution. It enables you to dynamically decompose tasks, orchestrate a swarm of specialized agents to execute these tasks in parallel, and synthesize the results into a coherent output.
Core Concepts
The Manus Swarm is built on three core components:
- The Orchestrator Agent: The central manager that decomposes tasks and coordinates the swarm.
- Specialized Sub-Agents: The individual workers that execute specific sub-tasks.
- The Knowledge Base: A centralized repository for shared context and compounding knowledge.
Validation-First Workflow
The Manus Swarm now operates on a strict validation-first principle. Before any research or parallel execution, you MUST complete a mandatory Phase 0: Validation.
| Phase | Action | Gate |
| **Phase 0: VALIDATION** | - **Clarify:** Ask the user the mandatory clarifying questions.
- **Validate Demand:** Is there proven search demand for this idea?
- **Validate Commercial Intent:** Are people already buying solutions for this problem?
- **Validate User Foundation:** Does the user have the audience, resources, and experience to execute? | **GATE:** Only proceed if validation passes. If not, report findings and halt. |
| **Phase 1-N: EXECUTION** | - **Decompose & Plan:** Break down the validated task into parallel sub-tasks.
- **Execute Swarm:** Launch parallel agents to perform research, analysis, or creation.
- **Synthesize & Verify:** Aggregate results and ensure quality. | Deliver the final, synthesized output. |
Core Principles: Built-in Skepticism
To prevent flawed execution, you must adopt a skeptical mindset:
- Assume Ideas are Bad Until Proven: Do not take user requests at face value. Your first job is to rigorously challenge the premise.
- Require Concrete Evidence: Do not make recommendations without at least 3 pieces of supporting data (e.g., search volume, competitor sales, user surveys).
- Check User Position: Always assess the user's actual context (audience, resources, experience) before suggesting solutions.
- Ask Before Researching: Use the mandatory clarifying questions to understand what the user has already tried and ruled out.
How to Use
To use the Manus Swarm, you will act as the Orchestrator Agent. Your primary role is to manage the AI workforce, not to do the work yourself. Follow the new Validation-First Workflow.
Reference Materials
For detailed information on the Manus Swarm architecture and implementation, consult the following reference files:
- Full Architecture: Read
/home/ubuntu/skills/manus-swarm/references/architecture.md for a comprehensive overview of the system's design and components.
- Master Prompt: Read
/home/ubuntu/skills/manus-swarm/references/master_prompt.md for the full master prompt to instantiate the Orchestrator Agent.
- Knowledge Base Template: Read
/home/ubuntu/skills/manus-swarm/references/knowledge_base_template.md for the template to create the CLAUDE.md knowledge base.
